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. 2019 May 29;9:8002. doi: 10.1038/s41598-019-44513-8

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Network analysis of the molecular degree of perturbation score values in all participants from India and China. A Spearman correlation matrix including the molecular degree of perturbation values for each biomarker as well as the single sample molecular degree of perturbation was built. (A) Network illustrates strong Spearman correlations (p < 0.001; Spearman rank value > 0.7 or <−0.7). Markers were clustered based on a similarity index of the correlation profiles using a modularity algorithm and depicted with Fruchterman Reingold (force-directed graph drawing). Using this approach, three main nodes were detected. Only markers which had strong correlations were plotted to reduce visual pollution. Red lines represent positive while blue lines indicate negative correlations. (B) Node analysis was used to illustrate the number of strong correlations per marker. Markers were grouped according to the number of connections from minimum to maximum numbers detected.